Named Data Network Security - This article provides an overview of the security of VANET, which is a vehicle network. When reviewing this topic, publications of various researchers were considered. The article provides information security requirements for VANET, an overview of security research, an overview of existing attacks, methods for detecting attacks and appropriate countermeasures against such threats.

Named Data Network Security - In networking, the data transmission rate is the coreelement to measure the network performance capability. A stable network infrastructure should support high transmission capacity with guaranteed network quality. In Named Data Networking (NDN), the performance of producer has been a hot topic to be discussed due to its transmission challenges. Hence in this paper, an analysis of transmission delay for single and multiple producers are discussed in detail. The simulation of network transmission delay for single producer and multiple producers is carried out using ndnSIM simulator. The factors that impacting network transmissions, such as sequence number and retransmission times are highlighted. The simulation results provide acceptable data to assist the development of more complextopology for NDN producers.

Named Data Network Security - This research focuses on the interest flooding attack model and its impact on the consumer in the Named Data Networking (NDN) architecture. NDN is a future internet network architecture has advantages compared to the current internet architecture. The NDN communication model changes the communication paradigm from a packet delivery model based on IP addresses to names. Data content needed is not directly taken from the provider but stored in a distributed manner on the router. Other consumer request data can served by nearest router. It will increase the speed of data access and reduce delay. The changes communication model also have an impact on the existing security system. One attack that may occur is the threat of a denial of service (DoS) known as an interest flooding attack. This attack makes the network services are being unavailable. This paper discussed examining the interest flooding attack model that occurred and its impact on the performance of NDN. The result shows that interest flooding attacks can decrease consumer satisfied interest.

Named Data Network Security - Named Data Networking (NDN) is a network with a future internet architecture that changes the point of view in networking from host-centric to data-centric. Named data networking provides a network system where the routing system is no longer dependent on traditional IP. Network packets are routed through nodes by name. When many manufacturers produce packages with different names for several consumers, routing with load balancing is necessary. The case study carried out is to conduct a simulation by connecting all UIN campuses into a topology with the name UIN Topology in Indonesia, using several scenarios to describe the effectiveness of the load balancer on the UIN topology in Indonesia. This study focuses on load balancer applications to reduce delays in Named Data Networking (NDN), the topology of UIN in Indonesia.

Named Data Network Security - The concept of the internet in the future will prioritize content, by reducing delays in data transmission. Named Data Networking (NDN) is a content-based future internet concept that changes the paradigm of using IP. Inside the NDN router, there are three data structures, namely Content Store (CS), Pending Interest Table (PIT), and Forwarding Information Base (FIB). Pending Interest Table (PIT) contains a list of unfulfilled interests. This condition occurs when the node has not received a response after the interest forwarding process. Measurable and fast PIT performance is a challenge in Named Data Networks. In this study, we will try to do a simulation to measure and analyze the performance of PIT in NDN in the Palapa Ring topology. The research was conducted using the NDNSim simulator, to see the performance in the PIT. The simulation and analysis of the results show that the granularity of a prefix has an effect on In Satisfied Interest in an NDN network. At the number of interests of 100, the result obtained from the simulation is that there is a decrease in the percentage of interest data served, amounting to more than 20\%. At the amount of interest in 1000 about more than 30\%. The length of the prefix and the number of interest sent by the consumer affect the performance of the PIT, seen from the number of In Satisfied Interests.

Named Data Network Security - With the growing recognition that current Internet protocols have significant security flaws; several ongoing research projects are attempting to design potential next-generation Internet architectures to eliminate flaws made in the past. These projects are attempting to address privacy and security as their essential parameters. NDN (Named Data Networking) is a new networking paradigm that is being investigated as a potential alternative for the present host-centric IP-based Internet architecture. It concentrates on content delivery, which is probably underserved by IP, and it prioritizes security and privacy. NDN must be resistant to present and upcoming threats in order to become a feasible Internet framework. DDoS (Distributed Denial of Service) attacks are serious attacks that have the potential to interrupt servers, systems, or application layers. Due to the probability of this attack, the network security environment is made susceptible. The resilience of any new architecture against the DDoS attacks which afflict today s Internet is a critical concern that demands comprehensive consideration. As a result, research on feature selection approaches was conducted in order to use machine learning techniques to identify DDoS attacks in NDN. In this research, features were chosen using the Information Gain and Data Reduction approach with the aid of the WEKA machine learning tool to identify DDoS attacks. The dataset was tested using KNearest Neighbor (KNN), Decision Table, and Artificial Neural Network (ANN) algorithms to categorize the selected features. Experimental results shows that Decision Table classifier outperforms well when compared to other classification algorithms with the with the accuracy of 85.42\% and obtained highest precision and recall score with 0.876 and 0.854 respectively when compared to the other classification techniques.

Named Data Network Security - With the continuous development of network technology as well as science and technology, artificial intelligence technology and its related scientific and technological applications, in this process, were born. Among them, artificial intelligence technology has been widely used in information detection as well as data processing, and has remained one of the current hot research topics. Those research on artificial intelligence, recently, has focused on the application of network security processing of data as well as fault diagnosis and anomaly detection. This paper analyzes, aiming at the network security detection of students real name data, the relevant artificial intelligence technology and builds the model. In this process, this paper firstly introduces and analyzes some shortcomings of clustering algorithm as well as mean algorithm, and then proposes a cloning algorithm to obtain the global optimal solution. This paper, on this basis, constructs a network security model of student real name data information processing based on trust principle and trust model.

Multiple Fault Diagnosis - To solve the problems of low fault diagnosis rate and poor efficiency of AC-DC drive traction converter, a fault diagnosis method based on improved multiscale permutation entropy and wavelet analysis is proposed based on the multiple fault characteristics of input current curve in frequency domain. Firstly, the curve of the traction converter is decomposed by wavelet transform, and the modal components of different time scales are obtained. Then the fault characteristic parameters of different components are calculated by improved multi-scale permutation entropy. Finally, the multivariable support vector machine algorithm based on decision tree is used to obtain the tree-like optimal fault interval surface through small sample training, so as to achieve the fault classification of traction converters. The experimental results show that this method can effectively distinguish the fault types of traction converters, and improve the accuracy and efficiency of fault diagnosis, which has good adaptability and practical significance.

Multiple Fault Diagnosis - Aiming at the difficulty of extracting fault features on the aircraft landing gear hydraulic system, traditional feature extraction methods rely heavily on expert knowledge, and the accuracy of fault diagnosis is difficult to guarantee. This paper combined convolutional neural network (CNN) and support vector machine classification algorithm (SVM) to propose a fault diagnosis model suitable for aircraft landing gear hydraulic system. The diagnosis model adopted the onedimensional multi-channel CNN network structure, took the original pressure signal of multiple nodes as input, adaptively extracts the feature value of the pressure signal through CNN, and built a multi-feature fusion layer to realize the feature fusion of the pressure signal of each node. Finally, input the fused features into the SVM classifier to complete the fault classification. In order to verify the proposed fault diagnosis model, a typical aircraft landing gear hydraulic system simulation model was built based on AMESim, and several typical fault types such as hydraulic pump leakage, actuator leakage, selector valve clogging and accumulator failure were simulated, and corresponding Fault type data set, and use overlapping sample segmentation for data enhancement. Experiments show that the diagnosis accuracy of the proposed fault diagnosis algorithm can reach 99.25\%, which can realize the adaptive extraction of the fault features of the aircraft landing gear hydraulic system, and the features after multidimensional fusion have better discrimination, compared with traditional feature extraction methods more effective and more accurate.

Multiple Fault Diagnosis - Traditional mechanical and electrical fault diagnosis models for high-voltage circuit breakers (HVCBs) encounter the following problems: the recognition accuracy is low, and the overfitting phenomenon of the model is serious, making its generalization ability poor. To overcome above problems, this paper proposed a new diagnosis model of HVCBs based on the multi-sensor information fusion and the multi-depth neural networks (MultiDNN). This approach used fifteen typical time-domain features extracted from signals of exciting coil current and angular displacement to indicate the operational state of HVCBs, and combined the multiple deep neural networks (DNN) to improve the accuracy and standard deviation. Six operational states were simulated based on the experimental platform, including normal state, two typical mechanical faults and four typical electrical faults, and the coil current and angular displacement signals are collected in each state to verify the effectiveness of the proposed model. The experimental results showed that, compared with the traditional fault diagnosis model, the Multi-DNN based on multi-sensor information fusion can be applied to finding a better equilibrium between underfitting and overfitting phenomenon of the model.

Multiple Fault Diagnosis - Multiple fault diagnosis is a challenging problem, especially for complex high-risk systems such as nuclear power plants. Multilevel Flow Models (MFM) is a powerful tool for identifying functional failures of complex process systems composing of mass, energy and information flows. The method of fault diagnosis based on MFM is generally based on the assumption that only a single fault occurs, and based on this, the Depth First Search (DFS) is adopted to identify the abnormal functions at the lower level of an MFM. This paper presents a method based on Multilevel Flow Models (MFM) for diagnosing multiple functionally related and coupled faults. An MFM model is firstly transformed into a reasoning Causal Dependency Graph (CDG) model according to a group of alarm events. The CDG model is further decoupled to generate causal trees by a DFS algorithm, each of which represents an overall explanation of a cause of alarm events. The paper presents a comparative analysis of cases. It proves that the method proposed in the paper can give more comprehensive diagnostic results than the existing method.

Multiple Fault Diagnosis - In this article, fault detection (FD) method for multiple device open-circuit faults (OCFs) in modified neutral-point- clamped (NPC) inverters has been introduced using Average Current Park Vector (ACPV) algorithm. The proposed FD design circuit is loadindependent and requires only the converter 3- phase output current. The validity of the results has been demonstrated for OCF diagnostics using a 3-level inverter with one faulty switch. This article examines ACPV techniques for diagnosing multiple fault switches on the single-phase leg of 3-step NPC inverter. This article discusses fault tolerance for a single battery or inverter switch during a standard, active level 3 NPC inverter with connected neutral points. The primary goal here is to detect and locate open circuits in inverter switches. As a result, simulations and experiments are used to investigate and validate a FD algorithm based on a current estimator and two fault localization algorithms based on online adaptation of the space vector modulation (S VM) and the pulse pattern injection principle. This technique was efficiently investigated and provides three-stage modified NPC signature table that accounts for all possible instances of fault. The Matlab / S imulink software is used to validate the introduced signature table for the convergence of permanent magnet motors.

Multiple Fault Diagnosis - In order to solve the problem of real-time fault diagnosis of UAV flight control system, a fault diagnosis method based on hybrid diagnosis engine is proposed. Aiming at the multiple fault modes and cross-linking relationships of each node in the flight control system, the system reference model is established by qualitative and quantitative methods, and then a corresponding domain model is established according to the flight control system of a specific model. Finally, the fault diagnosis reasoning engine based on the model and the hybrid diagnosis engine realizes the diagnosis of the current fault of the system. The results show that this method can determine the time and location of the fault in real time and accurately, which provides an effective guarantee for improving the efficiency of UAV fault diagnosis and improving the flight safety of UAV.

Multiple Fault Diagnosis - Bearings are key transmission parts that are extensively used in rolling mechanical and equipment. Bearing failures can affect the regular running of machines, in serious cases, can cause enormous losses in economy and personnel casualties. Therefore, it is important to implement the research of diagnosing bearing faults. In this paper, a bearing faults diagnosis method was developed based on multiple image inputs and deep convolutional neural network. Firstly, the 1Dvibration signal is transformed into three different types of two-dimensional images: time-frequency image, vibration grayscale image and symmetry dot pattern image, respectively. Enter them into multiple DCNNs separately. Finally, Finally, the nonlinear features of multiple DCNN outputs are fused and classified to achieve bearing fault diagnostics. The experimental results indicate that the diagnosis accuracy of this proposed method is 98.8\%, it can extract the fault features of vibration samples well, and it is an effective bearing fault diagnosis methodology.

Multiple Fault Diagnosis - Diagnosis of faults in logic circuit is essential to improve the yield of semiconductor circuit production. However, accurate diagnosis of adjacent multiple faults is difficult. In this paper, an idea for diagnosis of logic circuit faults using deep learning is proposed. In the proposed diagnosis idea, two adjacent faults can be accurately diagnosed using three deep learning modules. Once the modules are trained with data processed from fault simulation, the number of faults and the location of the faults are predicted by the modules from test responses of logic circuit. Experimental results of the proposed fault diagnosis idea show more than 96.4\% diagnostic accuracy.

Moving Target Defense - Synthetic aperture radar (SAR) is an effective remote sensor for target detection and recognition. Deep learning has a great potential for implementing automatic target recognition based on SAR images. In general, Sufficient labeled data are required to train a deep neural network to avoid overfitting. However, the availability of measured SAR images is usually limited due to high cost and security in practice. In this paper, we will investigate the relationship between the recognition performance and training dataset size. The experiments are performed on three classifiers using MSTAR (Moving and Stationary Target Acquisition and Recognition) dataset. The results show us the minimum size of the training set for a particular classification accuracy.

Moving Target Defense - As cyberattacks continuously threaten conventional defense techniques, Moving Target Defense (MTD) has emerged as a promising countermeasure to defend a system against them by dynamically changing attack surfaces of the system. MTD provides the system a state-of-art security mechanism that increases the attack cost or complexity of the system aiming for reducing vulnerabilities exposed to potential attackers. However, the notion of the proactive and dynamic systems adopting MTD services causes a substantial trade-off between system performance and security effectiveness, compared to conventional defense strategies. The MTD tactics accordingly result in performance degradation (e.g., interruptions of service availability) as one of the drawbacks caused by continuous mutations of the system configuration. Therefore, it is crucial to validate not only the security benefits against system threats but also quality-of-service (QoS) for clients when an MTDenabled system proactively continues to mutate attack surfaces. This paper contributes to (i) developing new security metrics; (ii) measuring both the performance degradation and security effectiveness against potential real attacks (i.e., scanning, HTTP flood, dictionary, and SQL injection attack); and (iii) comparing the proposed job management strategies (i.e., drop and switchover) from a performance and security perspective in a physical SDN testbed.

Moving Target Defense - Low (low altitude), slow (slow maneuvering) and small (small size)" targets such as drones pose a serious threat to airport flight safety and urban security, and there is an urgent need for effective detection. These targets have weak echoes and inconspicuous features, covered by strong clutter. Conventional radar data update rates are low with limited integration pulses, making detection extremely difficult. In this paper, the digital ubiquitous radar is used for long-time observation in order to improve the detection performance, and the high-order motion characteristics of low-altitude drone target are analyzed. The long-time integration method is proposed via Keystone transform (KT) and the enhanced fractional Fourier transform (EFRFT) to compensate the range and Doppler migrations simultaneously. Both simulation and real experiment using Lband digital ubiquitous radar are carried out to verify the performance of the proposed method. It is shown that the integration ability is better and the peak spectrum are more obvious compared with the traditional FFT-based moving target detection (MTD) and popular FRFT method.

Moving Target Defense - False Data Injection Attack(FDIA) is a typical network attack, which can bypass the Bad Data Detection(BDD) and affect State Estimation(SE), the estimation results is vital for power system, thus posing a great threat to the security of power system. In this paper, a new defense scheme is proposed, which is based on flexible switching of spare lines. By switching on the spare lines of some working transmission lines flexibly, the transmission line parameters in the power system topology can be changed, so as to reduce the possibility of FDIA. The impact of switching spare lines on power system operation and FDIA by ergodic method is analyzed. An optimization algorithm is designed to find the least system generator cost for power grid operator and the least attack space for attackers, this algorithm is tested in the IEEE 5-bus system and IEEE 30-bus system, and the results show that the scheme has a good performance in resisting FDIA.

Moving Target Defense - In the modern era, much of worldwide critical operations from a variety of different sectors are managed by industrial control systems (ICS). A typical ICS includes an extensive range of computerized devices, control systems, and networking appliances used to manage efficiently an industrial process across large geographical areas. ICS underpin sensitive and critical national infrastructures such as water treatment and energy production and transportation. The consequences of a successful attack against them can lead to shutting the infrastructure down which has major impacts such as production stoppages or safety implications for people, the environment, and assets. At the same time, running a process while the infrastructure is under attack or compromised also has safety implications, potentially catastrophic. This work-in-progress focuses on an adaptive approach, able to alter the defensive posture while providing assurances about operational capacity (or downgrading it) and safety. Our approach involves transforming policies from simply a means to enforce security requirements defined a priori, to adaptive objects that are capable to evolve in response to unfolding attacks. We use a case study of reconnaissance attacks and moving target defense as a means to realize such adaptive security policies.

Moving Target Defense - In recent years, many companies and organizations have introduced internal networks. While such internal networks propose availability and convenience, there have been many cases in which malicious outsiders have intruded on these local networks, and leaked customer information through cyber attacks. In addition, there have recently been reports of a type of attack called ”Advanced Persistent Threats (APT)”. Unlike conventional cyber attacks, these attacks target specific objectives. And they use sophisticated techniques to penetrate the target’s system. Once malware successes to intrude into the system, malware does not immediately attack the target but hides for a long time to investigate the system and gather information. Moving Target Defense, MTD is a technology that dynamically changes the configurations of systems targeted by cyber attacks. In this study, we implemented a model using a proxy-based network-level MTD to detect and quarantine malware in internal networks. And we can confirm that the proposed method is effective in the detection and quarantine of malware.

Moving Target Defense - The use of traditional defense mechanisms or intrusion detection systems presents a disadvantage for defenders against attackers since these mechanisms are essentially reactive. Moving target defense (MTD) has emerged as a proactive defense mechanism to reduce this disadvantage by randomly and continuously changing the attack surface of a system to confuse attackers. Although significant progress has been made recently in analyzing the security effectiveness of MTD mechanisms, critical gaps still exist, especially in maximizing security levels and estimating network reconfiguration speed for given attack power. In this paper, we propose a set of Petri Net models and use them to perform a comprehensive evaluation regarding key security metrics of Software-Defined Network (SDNs) based systems adopting a time-based MTD mechanism. We evaluate two use-case scenarios considering two different types of attacks to demonstrate the feasibility and applicability of our models. Our analyses showed that a time-based MTD mechanism could reduce the attackers’ speed by at least 78\% compared to a system without MTD. Also, in the best-case scenario, it can reduce the attack success probability by about ten times.

Moving Target Defense - Moving target detection algorithm plays a vital role in computer vision research. Moving object detection mainly processes video images to identify moving objects differently from the background. Moving target detection algorithm has an excellent application role, such as: used for security and forbidden area security. This paper presents an effective method for detecting moving targets. The authors combine the corner detection method with LK optical flow method. Afterimage preprocessing, image corner detection, finally, we use LK optical flow method to detect the movement of the moving object, and we can judge the movement direction of the moving object only by two frames of pictures. This method can judge the direction of moving objects only by two pictures frames and has an excellent performance in speed detection. In particular, in detecting small moving targets, the results of this method are noticeable.

Multifactor Authentication - Internet connected Children s toys are a type of IoT devices that the security community should pay particular attention. A cyber-predator may interact with or gather confidential data about children without being physically present if IoT toys are hacked. Authentication to verify user identity is essential for all internetconnected applications, where relying on single authentication is not considered safe, especially in children s applications. Children often use easy-to-guess passwords in smart applications associated with the Internet of Things (IoT) for children s toys. In this paper, we propose to activate multi-factor authentication on the IoTs for children s toys connected to the internet using companion applications. When changing the user s behaviour (by IP address, GPS, OS version, and browser), the child s identity must be verified by two-factor authentication to prevent unauthorized access to preserve the child s safety and privacy. This paper introduces multi-authentication mechanisms: a password and another authentication type, either mobile phone SMS, security token, digital certificate, or biometric authentication.

Multifactor Authentication - The article describes the development and integrated implementation of software modules of photo and video identification system, the system of user voice recognition by 12 parameters, neural network weights, Euclidean distance comparison of real numbers of arrays. The user s biometric data is encrypted and stored in the target folder. Based on the generated data set was developed and proposed a method for synthesizing the parameters of the mathematical model of convolutional neural network represented in the form of an array of real numbers, which are unique identifiers of the user of a personal computer. The training of the training model of multifactor authentication is implemented using categorical cross-entropy. The training sample is generated by adding distorted images by changing the receptive fields of the convolutional neural network. The authors have studied and applied features of simulation modeling of user authorization systems. The main goal of the study is to provide the necessary level of security of user accounts of personal devices. The task of this study is the software implementation of the synthesis of the mathematical model and the training neural network, necessary to provide the maximum level of protection of the user operating system of the device. The result of the research is the developed mathematical model of the software complex of multifactor authentication using biometric technologies, available for users of personal computers and automated workplaces of enterprises.